# Variational Low-Rank Adaptation for Personalized Impaired Speech Recognition

arXiv: https://arxiv.org/abs/2509.20397 · HTML: https://mlsanity.com/paper/2509.20397/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 20 of 20)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [DoRA: Weight-Decomposed Low-Rank Adaptation](https://mlsanity.com/paper/2402.09353/) (2024)
- [Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2303.15647/) (2023)
- [Bayesian Low-rank Adaptation for Large Language Models](https://mlsanity.com/paper/2308.13111/) (2023)
- [Uncertainty-guided Continual Learning with Bayesian Neural Networks](https://mlsanity.com/paper/1906.02425/) (2019)
- [Asymmetry in Low-Rank Adapters of Foundation Models](https://mlsanity.com/paper/2402.16842/) (2024)
- [Personalizing ASR for Dysarthric and Accented Speech with Limited Data](https://mlsanity.com/paper/1907.13511/) (2019)
- [LoRA Dropout as a Sparsity Regularizer for Overfitting Control](https://mlsanity.com/paper/2404.09610/) (2024)
- [MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2405.12130/) (2024)
- [Speaker adaptation for Wav2vec2 based dysarthric ASR](https://mlsanity.com/paper/2204.00770/) (2022)
- [On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches](https://mlsanity.com/paper/2211.08833/) (2022)
- [Bayesian-LoRA: LoRA based Parameter Efficient Fine-Tuning using Optimal Quantization levels and Rank Values trough Differentiable Bayesian Gates](https://mlsanity.com/paper/2406.13046/) (2024)
- [Parameter-efficient Dysarthric Speech Recognition Using Adapter Fusion and Householder Transformation](https://mlsanity.com/paper/2306.07090/) (2023)
- [Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank Adaptation](https://mlsanity.com/paper/2411.04358/) (2024)
- [Improving LoRA with Variational Learning](https://mlsanity.com/paper/2506.14280/) (2025)
- [Adapting Foundation Speech Recognition Models to Impaired Speech: A Semantic Re-chaining Approach for Personalization of German Speech](https://mlsanity.com/paper/2506.21622/) (2025)
- [A study on the impact of Self-Supervised Learning on automatic dysarthric speech assessment](https://mlsanity.com/paper/2306.04337/) (2023)
- [Hypernetworks for Personalizing ASR to Atypical Speech](https://mlsanity.com/paper/2406.04240/) (2024)

## Built on by (most cited first, 4 of 4)

- [When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition](https://mlsanity.com/paper/2605.02782/) (2026)
- [Demonstration of Adapt4Me: An Uncertainty-Aware Authoring Environment for Personalizing Automatic Speech Recognition to Non-normative Speech](https://mlsanity.com/paper/2603.20112/) (2026)
- [Choosing a PEFT Variant for Per-Patient Dysarthric ASR: A Single-Speaker Case Study on Two ASR Bases](https://mlsanity.com/paper/2609.02735/) (2026)
- [Understanding Personalization Gains in Whisper](https://mlsanity.com/paper/2610.17019/) (2026)

## Abstract (arXiv)

Speech impairments resulting from congenital disorders, such as cerebral palsy, down syndrome, or apert syndrome, as well as acquired brain injuries due to stroke, traumatic accidents, or tumors, present major challenges to automatic speech recognition (ASR) systems. Despite recent advancements, state-of-the-art ASR models like Whisper still struggle with non-normative speech due to limited training data availability and high acoustic variability. Moreover, collecting and annotating non-normative speech is burdensome: speaking is effortful for many affected individuals, while laborious annotation often requires caregivers familiar with the speaker. This work introduces a novel ASR personalization method based on Bayesian Low-rank Adaptation for data-efficient fine-tuning. We validate our method on the English UA-Speech dataset and a newly collected German speech dataset, BF-Sprache, from a child with structural speech impairment. The dataset and approach are designed to reflect the challenges of low-resource settings that include individuals with speech impairments. Our method significantly improves ASR accuracy for impaired speech while maintaining data and annotation efficiency, offering a practical path toward inclusive ASR.